Master Class in Quantitative Linguistics
March 4 - March 8, 2019
Baayen, R. H., and Divjak. D. (2017). Ordinal GAMMs: A New Window on Human Ratings. In Makarova, A., Dickey, S. M., and Divjak, D. (Eds.) Each Venture a New Beginning. Studies in Honor of Laura A. Janda. Bloomington, Slavica, 39-56
Baayen, R. H. and Linke, M. (2019). Introduction to the generalized additive model. Manuscript, University of Tuebingen.
Baayen, R. H., Rij, J. van, De Cat, C., and Wood, S. N. (2018). Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. In Speelman, D., Heylen, K., and Geeraerts, D. (Eds.) Mixed Effects Regression Models in Linguistics, (pages 49 - 69). Springer, Berlin.
Baayen, R. H., Vasishth, S., Kliegl, R., and Bates, D. (2017). The cave of Shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 206 - 234.
Roettger, T. B., Winter, B., and Baayen, R. H. (2018). Emergent data analysis in phonetic sciences: Towards pluralism and reproducibility. Journal of Phonetics, 73, 1-7.
Tomaschek, F., Hendrix, P., and Baayen, R. H. (2018). Strategies for addressing collinearity in multivariate linguistic data. Journal of Phonetics, 71, 249-267.
Arnold, D., Tomaschek, F., Sering, K., Lopez, F., and Baayen, R.H. (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLoS ONE 12(4): e0174623, 1-16.
Baayen, R. H., Milin, P., and Ramscar, M. (2016). Frequency in lexical processing. Aphasiology, 1174 - 1220.
Baayen, R. H., Chuang, Y. Y., and Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13 (2), 232-270.
Baayen, R. H., Chuang, Y. Y., Shafaei-Bajestan E., and Blevins, J. P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, 1-39.
Geeraert, K., Newman, J., and Baayen, R. H. (2017). Idiom variation: Experimental data and a blueprint of a computational model. In Christiansen, M., and Arnon, I. (Eds.) More than Words: The Role of Multiword Sequences in Language Learning and Use. Special issue of Topics in Cognitive Science, 9, 653-669.
Linke, M., Bröker, F., Ramscar, M., and Baayen, R. H. (2017). Are baboons learning "orthographic" representations? Probably not. PLoS ONE, 12 (8):e0183876.
Shafaei-Bajestan, E. and Baayen, R. H. (2018). Wide Learning for Auditory Comprehension. In Yegnanarayana, B. (Chair) Proceedings of Interspeech 2018, 966-970. Hyderabad, India: International Speech Communication Association (ISCA).
Statistically oriented research designs are often described as either confirmatory or exploratory in nature. Confirmatory approaches are structured around testing a hypothesis by means of theoretically motivated variables, whereas exploratory approaches are (corpus- or) data-driven used to describe the typical tendencies of certain data, and they often operate with a large number of potentially interesting variables. Applying exploratory techniques in the context of learner corpus research, the goal could be to detect co-occurrence patterns of linguistic features that characterize the differences between native and non-native writing, or to search for the most consistent grammatical differences between learners with different language backgrounds, thus revealing potential candidates for crosslinguistic influences. In this session, we will familiarize ourselves with two exploratory methods, Exploratory Factor Analysis (EFA) and Statistical Keyness Analysis. We will apply them to learner corpus data to see what they can tell us – and discuss what they do not tell.
Gabrielatos, Costas. 2018. Keyness analysis: nature, metrics and techniques. In C. Taylor & A. Marchi (eds.), Corpus Approaches to Discourse: A critical review, 225–258. Oxford: Routledge.
Kruger, Haidee & Bertus van Rooy. 2018. Register variation in written contact varieties of English. English World-Wide 39(2), 214–242.
Biber, Douglas. 2012. Register as a predictor of linguistic variation. Corpus Linguistics and Linguistic Theory 8(1), 9–37.
Leech, Geoffrey. 2006. New resources, or just better old ones? The Holy Grail of representativeness. In N. Nesselhauf & C. Biewer (eds.), Corpus Linguistics and the Web (Language and Computers 59), 133–149. London: Brill.